Here is a list of the Top 10 Examples of Big Data Applications:
1. Healthcare Sector
The medical field has access to vast amounts of data. Yet it is plagued by failures to use data to curb rising health care costs and inefficient systems that stifle faster and better health gains across the board.
This is mainly because the electronic data is unavailable, inadequate, or unusable. Additionally, medical databases that contain health-related information have made it difficult to link data that can show patterns useful in the medical field.
Other challenges related to Big Data include excluding patients from the decision-making process and using data from various readily available sensors.
Applications of big data in the Medical sector
Some hospitals use data collected from a mobile phone app from millions of patients to allow doctors to use evidence-based medicine instead of performing multiple medical/lab tests on every patient who walks into the hospital. A battery of tests can be effective but also expensive and usually ineffective.
Free public health data and Google Maps were used at the University of Florida to create visual data that enables faster identification and efficient analysis of health care information to track the spread of chronic disease. Obamacare also uses big data in a variety of ways. Big data providers in the industry include Recombinant Data, Humedica, Explorys, and Cerner.
2. Entertainment, Communication, and Media
As consumers expect, on-demand multimedia is increasing day by day in terms of formats and devices, so this arises some Big Data challenges in the field of communications, media, and entertainment, such as:
-
Collect, analyze and use consumer statistics
-
Use of mobile content and social network content
-
Understanding media content usage patterns in real-time.
Applications of big data in the communication, media, and entertainment industries
Organizations in this industry analyze customer data simultaneously alongside behavioral data to create detailed customer profiles that can be used to:
-
Create content for different audiences
-
Recommend content on demand
-
Measure the performance of your content
An example of big data in this industry is the Wimbledon Championships (YouTube Video), which uses Big Data to provide real-time sentiment analysis of tennis matches to TV, mobile, and web users.
Spotify, a well-known music player app, uses Hadoop big data analytics to collect data from its millions of users worldwide. It then uses the analyzed data to provide informed music recommendations to individual users.
Amazon Prime, which aims to provide a great customer experience by offering videos, music, and Kindle books in one place, also uses big data heavily.
Big data providers in the industry include Infochimps, Splunk, Pervasive Software, and Visible Measures.
3. Education
From a technical perspective, a significant challenge in the education industry is integrating big data from different sources and vendors and using it on platforms not designed for other data.
From a practical point of view, employees and institutions must learn new data management and analysis tools.
On the technical side, there are challenges with integrating data from different sources on different platforms and vendors that weren’t designed to work with each other. Politically, protecting the privacy and personal data associated with Big Data used for educational purposes is challenging.
Application of big data in education
Big data is used significantly in higher education. It is used to measure the effectiveness of teachers to ensure a pleasant experience for students and teachers. Teacher performance can be fine-tuned and measured by student population, subject matter, student demographics, student aspirations, behavior classification, and several other variables
4. Production and Natural Resources
The growing demand for natural resources, including oil, agricultural products, minerals, gas, metals, etc., has increased the volume, and complexity, of data that is difficult to process.
Likewise, large volumes of data from the manufacturing industry are unused. Inefficient use of this information prevents improvements in product quality, energy efficiency, reliability, and profit margins.
Applications of big data in this industry
In the natural resources industry, Big Data enables predictive decision-support modeling used to ingest and integrate large amounts of data from geospatial, graphical, textual, and temporal data.
Among other benefits, big data has also been used to address today’s manufacturing challenges and gain a competitive advantage.
5. Government
In this sector, the most significant challenges are the integration and interoperability of big data across different departments and affiliated organizations.
Application of big data in state administration
Big Data has many applications in public services, including energy research, financial market analysis, fraud detection, health-related research, and environmental protection.
Some more specific examples of big data in this sector are as follows:
Big data is used to analyze the large number of disability claims filed with the Social Security Administration (SSA), which come as unstructured data. Analytics process medical information quickly and efficiently for faster decision-making and to detect suspiciously.
The Food and Drug Administration (FDA) uses big data to detect and study food-related illnesses and disease patterns. This allows for faster response, which has led to more immediate treatment and fewer deaths.
6. Insurance
Some of the main problems are lack of personalized services, lack of personalized pricing, and lack of targeted services for new and specific market segments.
Application of big data in insurance
Big data is used in the industry to provide customer insights for transparent and more straightforward products by examining and predicting customer behavior through data derived from social media. Big Data also enables better customer retention from insurance companies.
Regarding receivables management, predictive analytics from Big Data offers faster services, as vast amounts of data can be analyzed mainly at the underwriting stage. Fraud detection has also been improved.
Big data providers in the industry include Sprint, Qualcomm, Octo Telematics, and The Climate Corp.
7. Retail and Wholesale
From traditional brick-and-mortar retailers and wholesalers to today’s e-commerce merchants, the industry has accumulated a lot of data over time. This data is not used enough to improve the customer experience. All the changes and improvements made were relatively slow.
Big data applications in retail and wholesale
Retail and wholesale stores continue to collect extensive data on customer loyalty, POS, store inventory, and local demographics.
At the 2014 Big Show retail conference in New York, companies such as Microsoft, Cisco, and IBM presented the need for the retail industry to use big data for analytics and other uses, including:
Optimized staffing through data from purchasing patterns, local events, etc.
8. Transportation
Passenger behavior has recently been influenced by vast amounts of data from location-based social networks and high-speed data from telecommunications. Unfortunately, research aimed at understanding travel behavior has not progressed as quickly.
In most places, transport demand models are still based on poorly understood new social media structures.
Application of big data in the transport industry
Use of big data by governments: traffic management, route planning, intelligent transport systems, congestion management (by predicting traffic conditions)
9. Energy and Utilities
Intelligent meter readers allow data to be collected almost every 15 minutes instead of once a day with old readers. This granular data is used to better analyze utility consumption, enabling better customer feedback and monitoring utility usage.
In utilities, using big data also enables better asset and workforce management, helping to spot errors and correct them as soon as possible before a complete failure occurs.
10. Banking and Securities
A study of 16 projects at 10 top investment and retail banks shows that industry challenges include: early warning of securities fraud, tick analysis, card fraud detection, audit trail archiving, corporate credit risk reporting, business visibility, customer transformation data, including social business analytics, IT traffic analytics, and IT compliance analytics.
Big Data Applications in the Banking and Securities Sector
The Securities and Exchange Commission (SEC) uses big data to monitor activity in financial markets. They currently use network analytics and natural language processors to detect illegal trading in financial markets.
Retailers, big banks, hedge funds, and other so-called “big boys” in financial markets use big data for business analytics in high-frequency trading, pre-trade decision support analysis, sentiment measurement, predictive analytics, etc.
The industry also relies heavily on big data for risk analysis, including; anti-money laundering and enterprise demand risk management.
Advanced Certification in Applied Data Science, Machine Learning & IoT By E&ICT Academy, IIT Guwahati
9 months
Certification in Data Science and Machine Learning With Python By The
IoT Academy Noida
180 Hrs
